Triple
T24592546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O'Higgins Lake |
E608573
|
entity |
| Predicate | hasLanguageVariantNameInArgentina |
P105995
|
FINISHED |
| Object | Lago San Martín |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lago San Martín | Statement: [O'Higgins Lake, hasLanguageVariantNameInArgentina, Lago San Martín]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageVariantNameInArgentina Context triple: [O'Higgins Lake, hasLanguageVariantNameInArgentina, Lago San Martín]
-
A.
hasNameInSpanish
Indicates that an entity is associated with a specific name expressed in the Spanish language.
-
B.
nameInArgentina
chosen
Indicates that an entity is known by a particular name specifically within the context of Argentina.
-
C.
hasLongNameInSpanish
Indicates that an entity is known by a long or extended name when expressed in the Spanish language.
-
D.
unitArgentina
Indicates a relationship where an entity is associated with, belongs to, or is characterized as a unit related to Argentina.
-
E.
hasNameInAymaraOrQuechua
Indicates that an entity is known by a specific name expressed in either the Aymara or Quechua language.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e2c4cf54248190af7b0c2d9ade9830 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a9dc63208190b70f57b9821a7241 |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:30 a.m.